Pathfinding in Random Partially Observable Environments with Vision-Informed Deep Reinforcement Learning. (arXiv:2209.04801v1 [cs.LG])

Deep reinforcement learning is a technique for solving problems in a variety
of environments, ranging from Atari video games to stock trading. This method
leverages deep neural network models to make decisions based on observations of
a given environment with the goal of maximizing a reward function that can
incorporate cost and rewards for reaching goals. With the aim of pathfinding,
reward conditions can include reaching a specified target area along with costs
for movement. In this work, multiple Deep …

arxiv environments observable random reinforcement reinforcement learning vision